vinn-offline
Routes VINN feature caching and non-interactive k-selection workflows for ACT++ BYOL/ResNet episode features.
Datos de origen
- Repositorio
- VectorSpaceLab/AREX-Skill
- Última actividad en el origen
- 26 de agosto de 2026 a las 16:31
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 12
- Forks
- 2
Opciones de instalación
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Revisa los archivos de origen
Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- name
- vinn-offline
- description
- Routes VINN feature caching and non-interactive k-selection workflows for ACT++ BYOL/ResNet episode features.
- disable-model-invocation
- true
- metadata
- {"disco-role":"operating"}
- license
- MIT
# vinn-offline
Use this sub-skill when the task is about caching image features for VINN or choosing a k value from cached episode features.
## Typical triggers
- "Cache BYOL features for the simulated cube dataset"
- "Choose k for VINN"
- "How are feature files named?"
- "Why does the raw k-selection script stop in IPython?"
## What this sub-skill covers
- Feature caching from per-camera ResNet18 checkpoints.
- Feature-file naming and layout for simulated and cotrain variants.
- Offline nearest-neighbor k selection over cached features.
- CUDA requirements and dataset-index assumptions for VINN preprocessing.
## What it excludes
- ACT/CNNMLP/Diffusion training and eval -> [policy-training](../policy-training/SKILL.md).
- Simulation episode generation / replay / visualization -> [simulation-data](../simulation-data/SKILL.md).
- Real-robot VINN deployment -> root troubleshooting only.
## Read these first
- [Workflow recipes](references/workflows.md)
- [Troubleshooting](references/troubleshooting.md)
- [Data formats](../../references/data-formats.md)
## Run this helper first
Before a long cache or k-selection job, use [check_vinn_stack.py](scripts/check_vinn_stack.py) to confirm the repo checkout imports and the CUDA backend is visible.
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